{"id":"W2781600007","doi":"10.2514/6.2018-0710","title":"Information Exchange Considerations for Effective Fusion among Heterogeneous Network Participants","year":2018,"lang":"en","type":"article","venue":"2018 AIAA Information Systems-AIAA Infotech @ Aerospace","topic":"Target Tracking and Data Fusion in Sensor Networks","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"ca_institutions":"Lockheed Martin (Canada)","funders":"","keywords":"Computer science; Information fusion; Fusion; Sensor fusion; Artificial intelligence","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01608582,0.0009965322,0.001196656,0.001010901,0.003363072,0.00599238,0.002323185,0.003357951,0.01058912],"category_scores_gemma":[0.05678161,0.0008651064,0.0007726423,0.00110991,0.002049367,0.01862492,0.004702896,0.003317918,0.001877413],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001849619,"about_ca_system_score_gemma":0.003275133,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00130189,"about_ca_topic_score_gemma":0.001252626,"domain_scores_codex":[0.9902403,0.004089133,0.0006566692,0.0009597425,0.00314432,0.0009098622],"domain_scores_gemma":[0.9620176,0.02448962,0.002113117,0.004875081,0.00575775,0.0007468729],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0005608384,0.000173413,0.001294659,0.000298893,0.00006847362,0.0005179488,0.001118986,0.05654964,0.009660386,0.8606966,0.00670886,0.06235138],"study_design_scores_gemma":[0.0001974887,0.0003733573,0.000889223,0.0001389074,0.0001577453,0.0006777043,0.00160004,0.3378896,0.01619423,0.6205401,0.02126341,0.00007812795],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02032266,0.0003293419,0.9497567,0.005256296,0.0002385453,0.0004244021,0.000103297,0.0001898123,0.02337903],"genre_scores_gemma":[0.8232251,0.0004356945,0.1648776,0.0008463963,0.0004552167,0.0006240411,0.0001894842,0.0001255367,0.00922083],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01608582,"threshold_uncertainty_score":0.08507097,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02288683880147677,"score_gpt":0.2530347326729529,"score_spread":0.2301478938714761,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}